PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics
Jerrin Bright, Bavesh Balaji, Yuhao Chen, David A Clausi, and John S, Zelek

TL;DR
PitcherNet is an innovative system that analyzes live baseball broadcast videos to extract detailed pitcher kinematics and pitch statistics, overcoming traditional data limitations and enhancing real-time performance analysis.
Contribution
It introduces a novel end-to-end approach combining player tracking, 3D human modeling, and kinematic analysis directly from broadcast videos, enabling real-time baseball analytics.
Findings
Achieves 96.82% accuracy in pitcher identification
Reduces joint position error by 1.8mm
Outperforms baseline methods in analytics
Abstract
In the high-stakes world of baseball, every nuance of a pitcher's mechanics holds the key to maximizing performance and minimizing runs. Traditional analysis methods often rely on pre-recorded offline numerical data, hindering their application in the dynamic environment of live games. Broadcast video analysis, while seemingly ideal, faces significant challenges due to factors like motion blur and low resolution. To address these challenges, we introduce PitcherNet, an end-to-end automated system that analyzes pitcher kinematics directly from live broadcast video, thereby extracting valuable pitch statistics including velocity, release point, pitch position, and release extension. This system leverages three key components: (1) Player tracking and identification by decoupling actions from player kinematics; (2) Distribution and depth-aware 3D human modeling; and (3) Kinematic-driven…
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Taxonomy
TopicsVideo Analysis and Summarization · Sports Analytics and Performance · Anomaly Detection Techniques and Applications
